Work

Intelligent task offloading for sustainable energy management in industrial IoT edge cloud systems

Year: 2026

Type: article

Abstract: Efficient task offloading is critical for the Industrial Internet of Things (IIoT), yet existing strategies often fail to address the complex inter-task dependencies inherent in real-world industrial workflows. Current approaches predominantly treat tasks as independent units, an oversight that leads to execution deadlocks, suboptimal resource scheduling, and system instability. To bridge this gap, this study introduces M-SIEGFENNet-TUA, a Multi-Scale Integrated Edge-Guided Finite Element Neural Network with Tactical Unit Algorithm, designed for sustainable and reliable task offloading in edge–cloud environments. The model integrates multi-scale analysis for feature extraction, edge-guided attention for prioritization, and finite element optimization for balanced resource allocation. Crucially, unlike standard models, M-SIEGFENNet-TUA explicitly manages task dependencies, ensuring that interconnected tasks are executed in an optimal sequence. Experimental results demonstrate reduced latency (120–160 ms), lower delay (cloud 50→7 ms), high task completion rates (up to 94%), and minimal energy consumption (52.7 J). Overall, the framework improves efficiency, reliability, and sustainability, aligning with SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities).

Source: Discover Sustainability

DOI: 10.1007/s43621-026-02624-z

Authors: Kranthi Kumar Singamaneni, Meghamala Bag, Tanmaya Bhoi, Abhijit Joshi, Soumya Ranjan Bhoi, Debabrata Dansana

Institutions: Symbiosis International University, Rajendra University, Balangir

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,390